@article{Zhang2025, 
author = {Yu-Wei Zhang and Yanqing Liu and Hongguang Yang and Hui Liu and Zhongping Ji and Mingqiang Wei and Yanzhao Chen and Caiming Zhang},
title = {MMRelief: Modeling multi-human relief from a single photograph},
year = {2025},
journal = {Computational Visual Media},
volume = {11},
number = {3},
pages = {531-548},
keywords = {relief modeling, depth reconstruction, human normal estimation, body occlusion},
url = {https://www.sciopen.com/article/10.26599/CVM.2025.9450394},
doi = {10.26599/CVM.2025.9450394},
abstract = {This study focuses on multi-human relief modeling using a single photograph. Although previous studies successfully modeled 3D humans from single photographs, they were limited to reconstructing 3D individuals and could not be applied to multi-human scenes with complex inter-body and outer-body occlusions. In this study, we introduce MMRelief, a novel solution that takes a significant step toward high-quality and generalized multi-human relief modeling. MMRelief uses a three-step approach to achieve its objectives. First, it predicts an occlusion-aware depth map based on ZoeDepth [12]. Subsequently, it predicts a detailed normal map using a photo-to-normal network. Finally, MMRelief combines the strengths of both maps and constructs human relief using depth-constrained normal integration. Experimental results demonstrate that MMRelief has achieved state-of-the-art performance in normal human estimation. It can handle different styles of human photos with varying poses and dresses while producing reliefs with accurate body occlusions, reasonable depth ordering, and faithful geometrical details. The project page is at https://github.com/yanqingliu3856/MMRelief.}
}